pub struct Collection<G: Scope, D, R: Semigroup = isize> {
    pub inner: Stream<G, (D, G::Timestamp, R)>,
}
Expand description

A mutable collection of values of type D

The Collection type is the core abstraction in differential dataflow programs. As you write your differential dataflow computation, you write as if the collection is a static dataset to which you apply functional transformations, creating new collections. Once your computation is written, you are able to mutate the collection (by inserting and removing elements); differential dataflow will propagate changes through your functional computation and report the corresponding changes to the output collections.

Each collection has three generic parameters. The parameter G is for the scope in which the collection exists; as you write more complicated programs you may wish to introduce nested scopes (e.g. for iteration) and this parameter tracks the scope (for timely dataflow’s benefit). The D parameter is the type of data in your collection, for example String, or (u32, Vec<Option<()>>). The R parameter represents the types of changes that the data undergo, and is most commonly (and defaults to) isize, representing changes to the occurrence count of each record.

Fields

inner: Stream<G, (D, G::Timestamp, R)>

The underlying timely dataflow stream.

This field is exposed to support direct timely dataflow manipulation when required, but it is not intended to be the idiomatic way to work with the collection.

Implementations

Creates a new Collection from a timely dataflow stream.

This method seems to be rarely used, with the as_collection method on streams being a more idiomatic approach to convert timely streams to collections. Also, the input::Input trait provides a new_collection method which will create a new collection for you without exposing the underlying timely stream at all.

Creates a new collection by applying the supplied function to each input element.

Examples
extern crate timely;
extern crate differential_dataflow;

use differential_dataflow::input::Input;

fn main() {
    ::timely::example(|scope| {
        scope.new_collection_from(1 .. 10).1
             .map(|x| x * 2)
             .filter(|x| x % 2 == 1)
             .assert_empty();
    });
}

Creates a new collection by applying the supplied function to each input element.

Although the name suggests in-place mutation, this function does not change the source collection, but rather re-uses the underlying allocations in its implementation. The method is semantically equivalent to map, but can be more efficient.

Examples
extern crate timely;
extern crate differential_dataflow;

use differential_dataflow::input::Input;

fn main() {
    ::timely::example(|scope| {
        scope.new_collection_from(1 .. 10).1
             .map_in_place(|x| *x *= 2)
             .filter(|x| x % 2 == 1)
             .assert_empty();
    });
}

Creates a new collection by applying the supplied function to each input element and accumulating the results.

This method extracts an iterator from each input element, and extracts the full contents of the iterator. Be warned that if the iterators produce substantial amounts of data, they are currently fully drained before attempting to consolidate the results.

Examples
extern crate timely;
extern crate differential_dataflow;

use differential_dataflow::input::Input;

fn main() {
    ::timely::example(|scope| {
        scope.new_collection_from(1 .. 10).1
             .flat_map(|x| 0 .. x);
    });
}

Creates a new collection containing those input records satisfying the supplied predicate.

Examples
extern crate timely;
extern crate differential_dataflow;

use differential_dataflow::input::Input;

fn main() {
    ::timely::example(|scope| {
        scope.new_collection_from(1 .. 10).1
             .map(|x| x * 2)
             .filter(|x| x % 2 == 1)
             .assert_empty();
    });
}

Creates a new collection accumulating the contents of the two collections.

Despite the name, differential dataflow collections are unordered. This method is so named because the implementation is the concatenation of the stream of updates, but it corresponds to the addition of the two collections.

Examples
extern crate timely;
extern crate differential_dataflow;

use differential_dataflow::input::Input;

fn main() {
    ::timely::example(|scope| {

        let data = scope.new_collection_from(1 .. 10).1;

        let odds = data.filter(|x| x % 2 == 1);
        let evens = data.filter(|x| x % 2 == 0);

        odds.concat(&evens)
            .assert_eq(&data);
    });
}

Creates a new collection accumulating the contents of the two collections.

Despite the name, differential dataflow collections are unordered. This method is so named because the implementation is the concatenation of the stream of updates, but it corresponds to the addition of the two collections.

Examples
extern crate timely;
extern crate differential_dataflow;

use differential_dataflow::input::Input;

fn main() {
    ::timely::example(|scope| {

        let data = scope.new_collection_from(1 .. 10).1;

        let odds = data.filter(|x| x % 2 == 1);
        let evens = data.filter(|x| x % 2 == 0);

        odds.concatenate(Some(evens))
            .assert_eq(&data);
    });
}

Replaces each record with another, with a new difference type.

This method is most commonly used to take records containing aggregatable data (e.g. numbers to be summed) and move the data into the difference component. This will allow differential dataflow to update in-place.

Examples
extern crate timely;
extern crate differential_dataflow;

use differential_dataflow::input::Input;

fn main() {
    ::timely::example(|scope| {

        let nums = scope.new_collection_from(0 .. 10).1;
        let x1 = nums.flat_map(|x| 0 .. x);
        let x2 = nums.map(|x| (x, 9 - x))
                     .explode(|(x,y)| Some((x,y)));

        x1.assert_eq(&x2);
    });
}

Joins each record against a collection defined by the function logic.

This method performs what is essentially a join with the collection of records (x, logic(x)). Rather than materialize this second relation, logic is applied to each record and the appropriate modifications made to the results, namely joining timestamps and multiplying differences.

#Examples

extern crate timely;
extern crate differential_dataflow;

use differential_dataflow::input::Input;

fn main() {
    ::timely::example(|scope| {
        // creates `x` copies of `2*x` from time `3*x` until `4*x`,
        // for x from 0 through 9.
        scope.new_collection_from(0 .. 10isize).1
             .join_function(|x|
                 //   data      time      diff
                 vec![(2*x, (3*x) as u64,  x),
                      (2*x, (4*x) as u64, -x)]
              );
    });
}

Brings a Collection into a nested scope.

Examples
extern crate timely;
extern crate differential_dataflow;

use timely::dataflow::Scope;
use differential_dataflow::input::Input;

fn main() {
    ::timely::example(|scope| {

        let data = scope.new_collection_from(1 .. 10).1;

        let result = scope.region(|child| {
            data.enter(child)
                .leave()
        });

        data.assert_eq(&result);
    });
}

Brings a Collection into a nested scope, at varying times.

The initial function indicates the time at which each element of the Collection should appear.

Examples
extern crate timely;
extern crate differential_dataflow;

use timely::dataflow::Scope;
use differential_dataflow::input::Input;

fn main() {
    ::timely::example(|scope| {

        let data = scope.new_collection_from(1 .. 10).1;

        let result = scope.iterative::<u64,_,_>(|child| {
            data.enter_at(child, |x| *x)
                .leave()
        });

        data.assert_eq(&result);
    });
}

Brings a Collection into a nested region.

This method is a specialization of enter to the case where the nested scope is a region. It removes the need for an operator that adjusts the timestamp.

Delays each difference by a supplied function.

It is assumed that func only advances timestamps; this is not verified, and things may go horribly wrong if that assumption is incorrect. It is also critical that func be monotonic: if two times are ordered, they should have the same order once func is applied to them (this is because we advance the timely capability with the same logic, and it must remain less_equal to all of the data timestamps).

Applies a supplied function to each update.

This method is most commonly used to report information back to the user, often for debugging purposes. Any function can be used here, but be warned that the incremental nature of differential dataflow does not guarantee that it will be called as many times as you might expect.

The (data, time, diff) triples indicate a change diff to the frequency of data which takes effect at the logical time time. When times are totally ordered (for example, usize), these updates reflect the changes along the sequence of collections. For partially ordered times, the mathematics are more interesting and less intuitive, unfortunately.

Examples
extern crate timely;
extern crate differential_dataflow;

use differential_dataflow::input::Input;

fn main() {
    ::timely::example(|scope| {
        scope.new_collection_from(1 .. 10).1
             .map_in_place(|x| *x *= 2)
             .filter(|x| x % 2 == 1)
             .inspect(|x| println!("error: {:?}", x));
    });
}

Applies a supplied function to each batch of updates.

This method is analogous to inspect, but operates on batches and reveals the timestamp of the timely dataflow capability associated with the batch of updates. The observed batching depends on how the system executes, and may vary run to run.

Examples
extern crate timely;
extern crate differential_dataflow;

use differential_dataflow::input::Input;

fn main() {
    ::timely::example(|scope| {
        scope.new_collection_from(1 .. 10).1
             .map_in_place(|x| *x *= 2)
             .filter(|x| x % 2 == 1)
             .inspect_batch(|t,xs| println!("errors @ {:?}: {:?}", t, xs));
    });
}

Attaches a timely dataflow probe to the output of a Collection.

This probe is used to determine when the state of the Collection has stabilized and can be read out.

Attaches a timely dataflow probe to the output of a Collection.

This probe is used to determine when the state of the Collection has stabilized and all updates observed. In addition, a probe is also often use to limit the number of rounds of input in flight at any moment; a computation can wait until the probe has caught up to the input before introducing more rounds of data, to avoid swamping the system.

Assert if the collection is ever non-empty.

Because this is a dataflow fragment, the test is only applied as the computation is run. If the computation is not run, or not run to completion, there may be un-exercised times at which the collection could be non-empty. Typically, a timely dataflow computation runs to completion on drop, and so clean exit from a program should indicate that this assertion never found cause to complain.

Examples
extern crate timely;
extern crate differential_dataflow;

use differential_dataflow::input::Input;

fn main() {
    ::timely::example(|scope| {
        scope.new_collection_from(1 .. 10).1
             .map(|x| x * 2)
             .filter(|x| x % 2 == 1)
             .assert_empty();
    });
}

The scope containing the underlying timely dataflow stream.

Returns the final value of a Collection from a nested scope to its containing scope.

Examples
extern crate timely;
extern crate differential_dataflow;

use timely::dataflow::Scope;
use differential_dataflow::input::Input;

fn main() {
    ::timely::example(|scope| {

        let data = scope.new_collection_from(1 .. 10).1;

        let result = scope.region(|child| {
            data.enter(child)
                .leave()
        });

        data.assert_eq(&result);
    });
}

Returns the value of a Collection from a nested region to its containing scope.

This method is a specialization of leave to the case that of a nested region. It removes the need for an operator that adjusts the timestamp.

Creates a new collection whose counts are the negation of those in the input.

This method is most commonly used with concat to get those element in one collection but not another. However, differential dataflow computations are still defined for all values of the difference type R, including negative counts.

Examples
extern crate timely;
extern crate differential_dataflow;

use differential_dataflow::input::Input;

fn main() {
    ::timely::example(|scope| {

        let data = scope.new_collection_from(1 .. 10).1;

        let odds = data.filter(|x| x % 2 == 1);
        let evens = data.filter(|x| x % 2 == 0);

        odds.negate()
            .concat(&data)
            .assert_eq(&evens);
    });
}

Assert if the collections are ever different.

Because this is a dataflow fragment, the test is only applied as the computation is run. If the computation is not run, or not run to completion, there may be un-exercised times at which the collections could vary. Typically, a timely dataflow computation runs to completion on drop, and so clean exit from a program should indicate that this assertion never found cause to complain.

Examples
extern crate timely;
extern crate differential_dataflow;

use differential_dataflow::input::Input;

fn main() {
    ::timely::example(|scope| {

        let data = scope.new_collection_from(1 .. 10).1;

        let odds = data.filter(|x| x % 2 == 1);
        let evens = data.filter(|x| x % 2 == 0);

        odds.concat(&evens)
            .assert_eq(&data);
    });
}

Trait Implementations

Arranges a stream of (Key, Val) updates by Key. Accepts an empty instance of the trace type. Read more

Arranges a stream of (Key, Val) updates by Key. Accepts an empty instance of the trace type. Read more

Arranges a stream of (Key, Val) updates by Key. Accepts an empty instance of the trace type. Read more

Arranges a stream of (Key, Val) updates by Key. Accepts an empty instance of the trace type. Read more

Arranges a stream of (Key, Val) updates by Key. Accepts an empty instance of the trace type. Read more

Arranges a stream of (Key, Val) updates by Key. Accepts an empty instance of the trace type. Read more

Arranges a collection of (Key, Val) records by Key. Read more

As arrange_by_key but with the ability to name the arrangement.

Arranges a collection of Key records by Key. Read more

As arrange_by_self but with the ability to name the arrangement.

Returns a copy of the value. Read more

Performs copy-assignment from source. Read more

As consolidate but with the ability to name the operator.

Aggregates the weights of equal records into at most one record. Read more

Aggregates the weights of equal records. Read more

Count for general integer differences. Read more

Counts the number of occurrences of each element. Read more

Count for general integer differences. Read more

Counts the number of occurrences of each element. Read more

Assign unique identifiers to elements of a collection. Read more

Iteratively apply logic to the source collection until convergence. Read more

Matches pairs (key,val1) and (key,val2) based on key and then applies a function. Read more

Matches pairs (key, val) and key based on key, producing the former with frequencies multiplied. Read more

Subtracts the semijoin with other from self. Read more

Matches pairs (key,val1) and (key,val2) based on key and yields pairs (key, (val1, val2)). Read more

Joins two arranged collections with the same key type. Read more

An unsafe variant of join_core where the result closure takes additional arguments for time and diff as input and returns an iterator over (data, time, diff) triplets. This allows for more flexibility, but is more error-prone. Read more

Computes the prefix sum for each element in the collection. Read more

Determine the prefix sum at each element of location.

As reduce with the ability to name the operator.

Applies a reduction function on records grouped by key. Read more

Solves for output updates when presented with inputs and would-be outputs. Read more

Applies group to arranged data, and returns an arrangement of output data. Read more

A threshold with the ability to name the operator.

Transforms the multiplicity of records. Read more

Reduces the collection to one occurrence of each distinct element. Read more

Distinct for general integer differences. Read more

Reduces the collection to one occurrence of each distinct element.

Reduces the collection to one occurrence of each distinct element. Read more

Reduces the collection to one occurrence of each distinct element. Read more

Distinct for general integer differences. Read more

Auto Trait Implementations

Blanket Implementations

Gets the TypeId of self. Read more

Immutably borrows from an owned value. Read more

Mutably borrows from an owned value. Read more

Returns the argument unchanged.

Calls U::from(self).

That is, this conversion is whatever the implementation of From<T> for U chooses to do.

The resulting type after obtaining ownership.

Creates owned data from borrowed data, usually by cloning. Read more

🔬 This is a nightly-only experimental API. (toowned_clone_into)

Uses borrowed data to replace owned data, usually by cloning. Read more

The type returned in the event of a conversion error.

Performs the conversion.

The type returned in the event of a conversion error.

Performs the conversion.